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Modeling and Forecasting Time Series of Compositional Data: A Generalized Dirichlet Power Steady Model

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Scalable Uncertainty Management (SUM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9310))

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Abstract

This paper presents GDPSM a power steady model (PSM) based on generalized Dirichlet observations for modeling and predicting compositional time series. The model’s unobserved states evolve according to the generalized Dirichlet conjugate prior distributions. The observations’ distribution is transformed into a set of Beta distributions each of which is re-parametrized as a unidimensional Dirichlet in its exponential form. We demonstrate that dividing the modeling problem into multiple smaller problems leads to more accurate predictions. We evaluate this model with the web service selection application. Specifically, we analyze the proportions of the quality classes that are assigned to the web services interactions. Our model is compared with another PSM that assumes Dirichlet observations. The experiments show promising results in terms of precision errors and standardized residuals.

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Author information

Authors and Affiliations

  1. Engineering and Computer Science, Concordia University, Montreal, Canada

    Mohamad Mehdi

  2. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada

    Nizar Bouguila & Jamal Bentahar

  3. ELectrical and Computer Engineering, Concordia University, Montreal, Canada

    Elise Epaillard

Authors
  1. Mohamad Mehdi
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  2. Elise Epaillard
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  3. Nizar Bouguila
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  4. Jamal Bentahar
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Corresponding author

Correspondence to Mohamad Mehdi .

Editor information

Editors and Affiliations

  1. Fakultät für Mathematik und Informatik, FernUniversität in Hagen, Hagen, Germany

    Christoph Beierle

  2. California Polytechnic State University, SAN LUIS OBISPO, USA

    Alex Dekhtyar

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Mehdi, M., Epaillard, E., Bouguila, N., Bentahar, J. (2015). Modeling and Forecasting Time Series of Compositional Data: A Generalized Dirichlet Power Steady Model. In: Beierle, C., Dekhtyar, A. (eds) Scalable Uncertainty Management. SUM 2015. Lecture Notes in Computer Science(), vol 9310. Springer, Cham. https://doi.org/10.1007/978-3-319-23540-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-23540-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23539-4

  • Online ISBN: 978-3-319-23540-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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